NLP has the potential to dramatically transform customer experiences
Natural language processing or NLP has the potential to dramatically transform customer experiences. It would allow machines to read and understand natural languages like English and respond with a meaningful reply. It would enable automation at a previously unachievable level, allowing machines and humans to actually communicate.
In the last few years, advancements in artificial intelligence (AI) and NLP have paved the way towards newer use cases for human-machine interactions in CX management. From conversational interfaces to automated transcriptions of call recordings in a contact centre, there is an NLP technology powering most new-age CX systems.
You can define natural language processing as a technology that allows humans and computers to interact, enabling computers to understand human beings, process and identify a response, and return this response in a form that is comprehensible to the human participant. Therefore, the functionality of an NLP engine can be segmented into three steps – understand, process, and respond. To achieve this, it utilises theories from the following disciplines:
As you can see, NLP is a complex interdisciplinary area of study, often involving technologies like speech recognition and text analytics to uncover its full potential.
CX management has always involved interacting with customers at scale and making sense of these communications, across multiple channels. NLP lets you convert this largely unstructured practice into a structured, formalised format – ready to pass through analytics, use as a trigger for automated events, etc. some of the key use cases for NLP in CX are:
Despite its incredible potential, NLP is yet to become a CX staple due to two challenges – accuracy issues and computing demand. Human language is extremely nuanced, and it evolves every day. It is very difficult to pre-program an NLP library that can keep up with the dynamic evolution of how people communicate. Second, in order to store and process such vast amounts of data, you need substantial computing power. That’s why NLP budgets are growing slowly but steadily, with most companies spending 10% more in 2020 vs. 2019, according to a 2020 NLP survey report. There is still a lot of room for adoption, with plenty of use cases in the CX discipline.
Here are a few other key findings from the report:
Typically, companies are held back by the lack of adequate in-house infrastructure and access to data science skills when it comes to NLP adoption. A single statement said in a natural language holds an incredible amount of data, from standalone keywords to sentence structure, from underlying sentiment to customer metadata. When you multiply this by thousands of customers speaking via tens of channels every day, there is a massive volume of data to parse.
That’s why most companies choose to partner with a specialised NLP company, with domain-specific expertise. You could, of course, leverage any of the open-source or commercial NLP libraries available to build your own solution – but this is a painstaking process. Some of the companies making significant strides in NLP for CX are:
Apart from this, most major contact centre providers today like Genesys, Dialpad, and RingCentral incorporate NLP technology into their conversational offerings, making their chatbots more intuitive and accurate.
Here is a quick checklist to aid NLP solution assessment and selection for your organisation:
Fortunately, advancements in AI and the availability of open-source libraries gives you a world of opportunities for NLP deployment either from scratch or out of the box.